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MONOGRAPH APPLICATIONS of DATA MINING by Ms. Mariya MONOGRAPH APPLICATIONS OF DATA MINING By Ms. Mariya Khurshid INTRODUCTION: Data Mining is defined by two words named data and mining, data is termed as raw data whereas mining means extraction, hence the data mining is defined as the process is used to extract the relevant or usable data from large amount of data. We can also say that it is the process of discovering of patterns from large datasets. It also involves the intersection methods of machine learning, statistics and data mining. Another name of data mining is Knowledge Discovery in Data. Data mining is widely used in varied areas. There are a number of commercial data mining system available today and yet there are many challenges in this field. In this monograph, we will discuss the applications and the trend of data mining. Data Mining Applications Financial Data Analysis Retail Industry Telecommunication Industry Biological Data Analysis Other Scientific Applications Intrusion Detection Financial Data Analysis The financial data in banking and financial industry is generally reliable and of high quality which facilitates systematic data analysis and data mining. Design and construction of data warehouses for multidimensional data analysis and data mining.Loan payment prediction and customer credit policy analysis.Classification and clustering of customers for targeted marketing. Retail Industry Data mining in retail industry helps in identifying customer buying patterns and trends that lead to improved quality of customer service and good customer retention and satisfaction. Here is the list of examples of data mining in the retail industry − Design and Construction of data warehouses based on the benefits of data mining. Multidimensional analysis of sales, customers, products, time and region. Analysis of effectiveness of sales campaigns Product recommendation and cross-referencing of items. Telecommunication Industry Data mining in telecommunication industry helps in identifying the telecommunication patterns, catch fraudulent activities, make better use of resource, and improve quality of service. Here is the list of examples for which data mining improves telecommunication services − Multidimensional Analysis of Telecommunication data. Fraudulent pattern analysis. Identification of unusual patterns. Multidimensional association and sequential patterns analysis. Mobile Telecommunication services. Use of visualization tools in telecommunication data analysis. Biological Data Analysis In recent times, we have seen a tremendous growth in the field of biology such as genomics, proteomics, functional Genomics and biomedical research. Biological data mining is a very important part of Bioinformatics. Following are the aspects in which data mining contributes for biological data analysis − Semantic integration of heterogeneous, distributed genomic and proteomic databases. Alignment, indexing, similarity search and comparative analysis multiple nucleotide sequences. Association and path analysis. Visualization tools in genetic data analysis. Other Scientific Applications The applications discussed above tend to handle relatively small and homogeneous data sets for which the statistical techniques are appropriate. Huge amount of data have been collected from scientific domains such as geosciences, astronomy, etc. A large amount of data sets is being generated because of the fast numerical simulations in various fields such as climate and ecosystem modeling, chemical engineering, fluid dynamics, etc. Following are the applications of data mining in the field of Scientific Applications − Data Warehouses and data preprocessing. Graph-based mining. Visualization and domain specific knowledge. Intrusion Detection In this world of connectivity, security has become the major issue. With increased usage of internet and availability of the tools and tricks for intruding and attacking network prompted intrusion detection to become a critical component of network administration. Here is the list of areas in which data mining technology may be applied for intrusion detection − Development of data mining algorithm for intrusion detection. Association and correlation analysis, aggregation to help select and build discriminating attributes. Analysis of Stream data. Distributed data mining. Visualization and query tools. .
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